卫星有效载荷的多目标多学科设计优化研究

在卫星有效载荷系统研究中,实施多目标多学科优化的可行性设计。首先,分析了开展卫星有效载荷多学科设计优化的关键技术。建立了包含天线、转发器、数据传输、可靠性、成本和质量的多学科分析模型。然后,应用多目标遗传算法对某卫星有效载荷的可靠性和成本进行多目标设计优化,获得最优解集。最后,运用多学科协同优化结合遗传算法进行可靠性单目标设计优化。研究结果表明:有效载荷的多目标多学科设计优化全面考虑了多个学科之间的关系,设计人员可按需选择其满意的优化结果,大幅提高设计效率;协同优化方法有助于实现学科自治、并行设计,提高设计的灵活性和缩短设计周期。     

Blended wing body structures in multidisciplinary pre-design

The structural analysis of blended wing body (BWB) aircraft configurations is presented in the context of a preliminary, multidisciplinary aircraft design process by means of the aircraft design and optimization program (PrADO) of the Institut of Aircraft Design and Lightweight Structures of the TU Braunschweig. A multidisciplinary process is described that enables parametric creation of detailed finite element models and its loads. Iteratively different flight conditions are trimmed and corresponding pressure distributions calculated by the higher-order subsonic and supersonic panel code HISSS. Each defined loading condition is used for the iterative structural sizing of the primary structure. Based on finite element idealization, a mass estimation of all structural masses is performed. The primary and secondary masses are fed back into the closed overall aircraft optimization loop of PrADO until this iterative procedure shows convergence on calculated aircraft variables (e.g., aircraft masses and static engine thrust). This automated process enables further configuration improvements using manual parametric variations or optimization features of PrADO with an objective function being defined by fuel consumption, aircraft mass, or direct operating costs. Different structural solutions and their integration in the global model are presented for passenger versions of a 700 passenger BWB with special consideration of a pressurized cabin. As an example, structural masses and parametric studies on the influence of the center body rib spacing are presented and compared by weight breakdowns.     

An integrated multidisciplinary particle swarm optimization approach to conceptual ship design

A particle swarm optimization (PSO) solver is developed based on theoretical information available from the literature. The implementation is validated by utilizing the PSO optimizer as a driver for a single discipline optimization and for a multicriterion optimization and comparing the results to a commercially available gradient based optimization algorithm, previously published results, and a simple sequential Monte Carlo model. A typical conceptual ship design statement from the literature is employed for developing the single discipline and the multicriterion benchmark optimization statements. In the main new effort presented in this paper, an approach is developed for integrating the PSO algorithm as a driver at both the top and the discipline levels of a multidisciplinary design optimization (MDO) framework which is based on the Target Cascading (TC) method. The integrated MDO/PSO algorithm is employed for analyzing a multidiscipline optimization statement reflecting the conceptual ship design problem from the literature. Results are compared to MDO analyses performed when a gradient based optimizer comprised the optimization driver at all levels. The results, the strengths, and the weaknesses of the integrated MDO/PSO algorithm are discussed as related to conceptual ship design.     

基于协同进化博弈的多学科设计优化

复杂系统的设计问题可以非层次分解为并行的多个子空间优化设计问题。多学科优化的迭代过程可看成子空间博弈的过程。各冲突子目标协商一致条件下,子空间合作博弈的均衡点能达成原系统的整体最优,并给出协同进化算法求解博弈的Nash均衡点的计算框架。以某型民用客机的总体优化设计为例,将其分解成气动和重量两个子空间优化。设计变量不重叠地分布于各子空间,两冲突子目标分配相同权值,线性加权组合而形成的单目标作为各子空间共同的优化目标。计算结果表明此方法是有效的。     

基于多模型拓扑优化方法的车身结构概念设计

为获得最优的初始设计方案,在车身概念设计阶段对车身结构进行拓扑优化。车身结构性能指标综合考虑整体刚度、局部动态刚度和碰撞性能,采用多模型优化(multi-model optimization,MMO)方法解决此类复杂工况的拓扑优化问题,通过调节设计空间和设置参数,获得车身最优载荷路径。根据拓扑优化结果初步形成车身框架结构,可为后期详细设计提供参考。     

A surrogate based multistage-multilevel optimization procedure for multidisciplinary design optimization

Optimization procedure is one of the key techniques to address the computational and organizational complexities of multidisciplinary design optimization (MDO). Motivated by the idea of synthetically exploiting the advantage of multiple existing optimization procedures and meanwhile complying with the general process of satellite system design optimization in conceptual design phase, a multistage-multilevel MDO procedure is proposed in this paper by integrating multiple-discipline-feasible (MDF) and concurrent subspace optimization (CSSO), termed as MDF-CSSO. In the first stage, the approximation surrogates of high-fidelity disciplinary models are built by disciplinary specialists independently, based on which the single level optimization procedure MDF is used to quickly identify the promising region and roughly locate the optimum of the MDO problem. In the second stage, the disciplinary specialists are employed to further investigate and improve the baseline design obtained in the first stage with high-fidelity disciplinary models. CSSO is used to organize the concurrent disciplinary optimization and system coordination so as to allow disciplinary autonomy. To enhance the reliability and robustness of the design under uncertainties, the probabilistic version of MDF-CSSO (PMDF-CSSO) is developed to solve uncertainty-based optimization problems. The effectiveness of the proposed methods is verified with one MDO benchmark test and one practical satellite conceptual design optimization problem, followed by conclusion remarks and future research prospects.     

An inverse-measure-based unilevel architecture for reliability-based design optimization

Reliability-based design optimization (RBDO) is a methodology for finding optimized designs that are characterized with a low probability of failure. Primarily, RBDO consists of optimizing a merit function while satisfying reliability constraints. The reliability constraints are constraints on the probability of failure corresponding to each of the failure modes of the system or a single constraint on the system probability of failure. The probability of failure is usually estimated by performing a reliability analysis. During the last few years, a variety of different formulations have been developed for RBDO. Traditionally, these have been formulated as a double-loop (nested) optimization problem. The upper level optimization loop generally involves optimizing a merit function subject to reliability constraints, and the lower level optimization loop(s) compute(s) the probabilities of failure corresponding to the failure mode(s) that govern(s) the system failure. This formulation is, by nature, computationally intensive. Researchers have provided sequential strategies to address this issue, where the deterministic optimization and reliability analysis are decoupled, and the process is performed iteratively until convergence is achieved. These methods, though attractive in terms of obtaining a workable reliable design at considerably reduced computational costs, often lead to premature convergence and therefore yield spurious optimal designs. In this paper, a novel unilevel formulation for RBDO is developed. In the proposed formulation, the lower level optimization (evaluation of reliability constraints in the double-loop formulation) is replaced by its corresponding first-order Karush–Kuhn–Tucker (KKT) necessary optimality conditions at the upper level optimization. Such a replacement is computationally equivalent to solving the original nested optimization if the lower level optimization problem is solved by numerically satisfying the KKT conditions (which is typically the case). It is shown through the use of test problems that the proposed formulation is numerically robust (stable) and computationally efficient compared to the existing approaches for RBDO.     

Multidisciplinary collaborative optimization using fuzzy satisfaction degree and fuzzy sufficiency degree model

Collaborative optimization (CO) is a bi-level multidisciplinary design optimization (MDO) method for large-scale and distributed-analysis engineering design problems. Its architecture consists of optimization at both the system-level and autonomous discipline levels. The system-level optimization maintains the compatibility among coupled subsystems. In many engineering design applications, there are uncertainties associated with optimization models. These will cause the design objective and constraints, such as weight, price and volume, and their boundaries, to be fuzzy sets. In addition the multiple design objectives are generally not independent of each other, that makes the decision-making become complicated in the presence of conflicting objectives. The above factors considerably increase the modeling and computational difficulties in CO. To relieve the aforementioned difficulties, this paper proposes a new method that uses a fuzzy satisfaction degree model and a fuzzy sufficiency degree model in optimization at both the system level and the discipline level. In addition, two fuzzy multi-objective collaborative optimization strategies (Max–Min and α-cut method) are introduced. The former constructs the sufficiency degree for constraints and the satisfaction degree for design objectives in each discipline respectively, and adopts the Weighted Max–Min method to maximize an aggregation of them. The acceptable level is set up as the shared design variable between disciplines, and is maximized at the system level. In the second strategy, the decision-making space of the constraints is distributed in each discipline independently through the allocation of the levels of α. At the system level, the overall satisfaction degree for all disciplines is finally maximized. The illustrative mathematical example and engineering design problem are provided to demonstrate the feasibility of the proposed methods.     

Design optimization of containers for sloshing and impact

A multidisciplinary design and optimization (MDO) method is presented to support the design process of partially filled liquid containers subject to the disciplines of sloshing and impact analyses. Experimental techniques are used to understand sloshing as a phenomenon and to evaluate the computational fluid dynamics code. Validation includes qualitative comparison of visual free-surface behavior and quantitative comparisons of pressure measurements in the time and frequency domain. The liquid motion exhibits good comparisons in time. Deviations are caused by both the experimental signal filtration process and deficiencies in the low-frequency measurement capability of the accelerometer. The first two odd oscillatory modes are accurately captured. An objective function for the quantitative evaluation of the sloshing phenomenon is proposed. For impact, the von Mises baffle stress is used. Single and multidisciplinary optimization formulations using LS-OPT are presented and examined. The multidisciplinary optimum proved to be a compromise between the optima obtained when considering the two single disciplines independently.     

Metamodel-based collaborative optimization framework

This paper focuses on the metamodel-based collaborative optimization (CO). The objective is to improve the computational efficiency of CO in order to handle multidisciplinary design optimization problems utilising high fidelity models. To address these issues, two levels of metamodel building techniques are proposed: metamodels in the disciplinary optimization are based on multi-fidelity modelling (the interaction of low and high fidelity models) and for the system level optimization a combination of a global metamodel based on the moving least squares method and trust region strategy is introduced. The proposed method is demonstrated on a continuous fiber-reinforced composite beam test problem. Results show that methods introduced in this paper provide an effective way of improving computational efficiency of CO based on high fidelity simulation models.     

Collaborative optimization with disciplinary conceptual design

For the first time, a multilevel optimization approach with disciplinary conceptual design is demonstrated. Collaborative optimization is used to decompose an example bridge design problem among two groups of designers – a superstructure design group and a deck design group. The disciplinary groups are allowed to search over different design concepts and formulate the design variables and constraints for each. The autonomy of the two groups is managed by a system-level group which insures that overall system objectives are met and coupling is properly accounted for. Even though discrete conceptual design occurs within the disciplinary groups, a continuous gradient-based optimization algorithm is used at the system level. The procedure was started from a nonoptimal concept, and converged to the optimal concept. Received September 9, 1999     

Ubiquitous conceptual design of a ubiquitous application: A textile SME case study for real time manufacturing monitoring

Advances in Information and Communication Technologies (ICT), computing, networking, mechanics and electronics are changing the people’s way of life. Several research efforts are leading the design and development of Artifact and Service Combination (ASC) with the implementation of Ubiquitous Technologies (UTs) in multidisciplinary sectors. However, the design process of such systems often ends in the implementation of conventional approaches and tools. A Ubiquitous Design Support Environment (UDSE) comprising an application intended to guide the different activities, tools and resources applied at the conceptual design stage is presented. After needs analysis, multidisciplinary collaborations are also required in order to generate innovative conceptual solutions, focusing this approach in the conceptual design stage of traditional design methods. Some activities from the conceptual design stage are enhanced through the use of the UDSE as well as through the use of a novel ubiquity assessment tool for concept selection and validation of Ubiquitous Products and Services. Finally, a case study on a Small and Medium Enterprise (SME) from textile sector, in a developing country, is presented to analyze and validate the presented concepts.     

面向分级设计优化的飞行器参数化建模方法

针对飞行器气动隐身外形综合设计优化问题,提出合适的面向分级设计优化流程,建立适应该流程的渐进分层参数化建模方法;用基于敏度分析的参数影响程度分析方法筛选复杂设计变量;采用多学科设计优化（Multidisplinary Design Optimization,MDO）理论和差分进化算法进行飞行器气动隐身外形的综合设计优化.将该方法用于某飞行器外形设计优化,结果表明：该方法合理可行,可为飞行器外形多学科设计优化提供一定参考.     

高性能计算在多学科大变量飞机优化设计中的应用

波音、空客等公司飞机设计中采用的先进设计手段之一是基于高性能计算的多学科、大规模设计变量优化的应用。目前我国的高性能计算在硬件方面已处于世界领先水平,但在工程应用系统方面还无法满足需求。文章介绍了以工程实际需求为导向,以高性能计算资源为辅助手段,基于飞机设计需求而开发的多学科、大变量的并行计算软件系统及计算平台,以及利用该平台实现的航空应用算例。     

Holistic ship design optimization

Ship design is a complex endeavor requiring the successful coordination of many disciplines, of both technical and non-technical nature, and of individual experts to arrive at valuable design solutions. Inherently coupled with the design process is design optimization, namely the selection of the best solution out of many feasible ones on the basis of a criterion, or rather a set of criteria. A systemic approach to ship design may consider the ship as a complex system integrating a variety of subsystems and their components, for example, subsystems for cargo storage and handling, energy/power generation and ship propulsion, accommodation of crew/passengers and ship navigation. Independently, considering that ship design should actually address the whole ship’s life-cycle, it may be split into various stages that are traditionally composed of the concept/preliminary design, the contractual and detailed design, the ship construction/fabrication process, ship operation for an economic life and scrapping/recycling. It is evident that an optimal ship is the outcome of a holistic optimization of the entire, above-defined ship system over her whole life-cycle. But even the simplest component of the above-defined optimization problem, namely the first phase (conceptual/preliminary design), is complex enough to require to be simplified (reduced) in practice. Inherent to ship design optimization are also the conflicting requirements resulting from the design constraints and optimization criteria (merit or objective functions), reflecting the interests of the various ship design stake holders.The present paper provides a brief introduction to the holistic approach to ship design optimization, defines the generic ship design optimization problem and demonstrates its solution by use of advanced optimization techniques for the computer-aided generation, exploration and selection of optimal designs. It discusses proposed methods on the basis of some typical ship design optimization problems with multiple objectives, leading to improved and partly innovative designs with increased cargo carrying capacity, increased safety and survivability, reduced required powering and improved environmental protection. The application of the proposed methods to the integrated ship system for life-cycle optimization problem remains a challenging but straightforward task for the years to come.     

Numerical integration in statistical decision-theoretic methods for robust design optimization

The Bayes principle from statistical decision theory provides a conceptual framework for quantifying uncertainties that arise in robust design optimization. The difficulty with exploiting this framework is computational, as it leads to objective and constraint functions that must be evaluated by numerical integration. Using a prototypical robust design optimization problem, this study explores the computational cost of multidimensional integration (computing expectation) and its interplay with optimization algorithms. It concludes that straightforward application of standard off-the-shelf optimization software to robust design is prohibitively expensive, necessitating adaptive strategies and the use of surrogates.     

数据库逻辑设计性能优化关键技术研究

数据库逻辑结构设计是把概念结构转化为具体DBMS所支持的逻辑模型,对初步设计的逻辑模型进行调整、修改和优化,是逻辑结构设计的重要工作。文中从三个方面研究逻辑设计性能优化关键技术。首先,通过四个定义研究消除依赖的方法,包括函数依赖、部分函数依赖、传递函数依赖、第三范式;其次,研究主关键字,包括聚簇索引的创建以及设计主关键字的通用规则;最后,研究关系的分解与合并,其中分解包括水平分解和垂直分解。文中研究的性能优化技术与通常的性能优化技术既有一致性也有冲突,实际运用要通过具体分析采用切实可行的策略。     

A reliability-based multidisciplinary design optimization procedure based on combined probability and evidence theory

To address the reliability-based multidisciplinary design optimization (RBMDO) problem under mixed aleatory and epistemic uncertainties, an RBMDO procedure is proposed in this paper based on combined probability and evidence theory. The existing deterministic multistage-multilevel multidisciplinary design optimization (MDO) procedure MDF-CSSO, which combines the multiple discipline feasible (MDF) procedure and the concurrent subspace optimization (CSSO) procedure to mimic the general conceptual design process, is used as the basic framework. In the first stage, the surrogate based MDF is used to quickly identify the promising reliable regions. In the second stage, the surrogate based CSSO is used to organize the disciplinary optimization and system coordination, which allows the disciplinary specialists to investigate and optimize the design with the corresponding high-fidelity models independently and concurrently. In these two stages, the reliability-based optimization both in the system level and the disciplinary level are computationally expensive as it entails nested optimization and uncertainty analysis. To alleviate the computational burden, the sequential optimization and mixed uncertainty analysis (SOMUA) method is used to decompose the traditional double-level reliability-based optimization problem into separate deterministic optimization and mixed uncertainty analysis sub-problems, which are solved sequentially and iteratively until convergence is achieved. By integrating SOMUA into MDF-CSSO, the Mixed Uncertainty based RBMDO procedure MUMDF-CSSO is developed. The effectiveness of the proposed procedure is testified with one simple numerical example and one MDO benchmark test problem, followed by some conclusion remarks.     

Nearly-orthogonal sampling and neural network metamodel driven conceptual design of multistage space launch vehicle

This paper presents a new design methodology for efficient conceptual design of complex systems involving multidisciplinary and computationally intensive analysis with large number of design variables. A nearly-orthogonal sampling of design space is proposed with good space filling properties to extract maximum useful information about system behavior using much lower number of trial designs. This sampled data is then used as training data for artificial neural network, which will act as a metamodel to represent the time consuming disciplinary analyses. A stage-wise interconnection of separate neural networks is also proposed for trajectory metamodel to offset dimensionality curse of neural networks. Genetic Algorithm performs global optimization by utilizing this metamodel and subsequently sequential quadratic programming performs the local optimization utilizing exact analyses. The performance of proposed methodology is investigated in this paper for the conceptual design optimization of multistage solid fueled space launch vehicle. The results show excellent approximation of highly non-linear functions using proposed sampling and drastic reduction in overall design optimization time, due to greatly reduced number of exact disciplinary analyses.